4 citations · 17 across the 12 of their papers we have counts for
13 papers
PromptDresser: Improving the Quality and Controllability of Virtual Try-On via Generative Textual Prompt and Prompt-aware Mask
Jeongho Kim, Hoiyeong Jin, Sunghyun Park +1
Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prom…
What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer
Chaeyeon Chung, Sunghyun Park, Jeongho Kim +1
Hairstyle transfer is a challenging task in the image editing field that modifies the hairstyle of a given face image while preserving its other appearance and background features.…
When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection
Dongmin Kim, Sunghyun Park, Jaegul Choo
Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evo…
StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
Jeongho Kim, Gyojung Gu, Minho Park +2
Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the…
Expression Domain Translation Network for Cross-domain Head Reenactment
Taewoong Kang, Jeongsik Oh, Jaeseong Lee +2
Despite the remarkable advancements in head reenactment, the existing methods face challenges in cross-domain head reenactment, which aims to transfer human motions to domains outs…
Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts
Sunghyun Park, Seunghan Yang, Jaegul Choo +1
Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-by-batch manner during inference. While label distributions often exhibit imbalances in…
Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
Kangyeol Kim, Sunghyun Park, Junsoo Lee +1
Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and en…
Improving Scene Text Recognition for Character-Level Long-Tailed Distribution
Sunghyun Park, Sunghyo Chung, Jungsoo Lee +1
Despite the recent remarkable improvements in scene text recognition (STR), the majority of the studies focused mainly on the English language, which only includes few number of ch…
RobustSwap: A Simple yet Robust Face Swapping Model against Attribute Leakage
Jaeseong Lee, Taewoo Kim, Sunghyun Park +2
Face swapping aims at injecting a source image's identity (i.e., facial features) into a target image, while strictly preserving the target's attributes, which are irrelevant to id…
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment
Taewoo Kim, Chaeyeon Chung, Yoonseo Kim +3
Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often…
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions
Sangyun Lee, Gyojung Gu, Sunghyun Park +2
Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing item. To solve the task, the existing methods warp the clothing item to fit the person's…
HairFIT: Pose-Invariant Hairstyle Transfer via Flow-based Hair Alignment and Semantic-Region-Aware Inpainting
Chaeyeon Chung, Taewoo Kim, Hyelin Nam +4
Hairstyle transfer is the task of modifying a source hairstyle to a target one. Although recent hairstyle transfer models can reflect the delicate features of hairstyles, they stil…
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
Kangyeol Kim, Sunghyun Park, Jaeseong Lee +3
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation mode…